A modular approach for integrative analysis of large-scale gene-expression and drug-response data
A modular approach for integrative analysis of large-scale gene-expression and drug-response data
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DOI：
10.1038/nbt1397
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发表时间：
2008-05-01
影响因子：
46.9
通讯作者：
Bergmann, Sven
中科院分区：
文献类型：
--
作者：
Kutalik, Zoltan;Beckmann, Jacques S.;Bergmann, Sven
高通量技术现在被用于从相同的生物样品中产生一种以上的数据。为了正确整合这些数据，我们建议使用co-modules，它描述了配对数据集的连贯模式，并构思了几种模块化方法来识别它们。我们首先使用计算机数据测试这些方法，证明我们的乒乓算法的综合方案在考虑噪声或复杂数据时更准确地揭示了药物-基因关联。第二，我们提供了一个广泛的比较研究，使用基因表达和药物反应的数据从NCI-60细胞系。利用DrugBank和Connectivity Map数据库中的信息，我们发现乒乓算法预测药物-基因关联的效果明显优于其他方法。共同模块为广泛的药物提供了可能的作用机制的见解，并提出了新的治疗靶点。
High-throughput technologies are now used to generate more than one type of data from the same biological samples. To properly integrate such data, we propose using co-modules, which describe coherent patterns across paired data sets, and conceive several modular methods for their identification. We first test these methods using in silico data, demonstrating that the integrative scheme of our Ping-Pong Algorithm uncovers drug-gene associations more accurately when considering noisy or complex data. Second, we provide an extensive comparative study using the gene-expression and drug-response data from the NCI-60 cell lines. Using information from the DrugBank and the Connectivity Map databases we show that the Ping-Pong Algorithm predicts drug-gene associations significantly better than other methods. Co-modules provide insights into possible mechanisms of action for a wide range of drugs and suggest new targets for therapy.